
# Step1. 导入数据文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('./Sample - Superstore.xls',sheet_name='Orders')
df.head()


# Step2. 数据分析：对Sub-Category进行分组，再对每组的Profit求和（数据聚合与分组操作——GroupBy机制）
data = df.groupby('Sub-Category')['Profit'].sum()
# df['Profit'].groupby(df['Sub-Category']).sum()  # 另一种写法
print(data)  # 返回Series：左侧是索引index，右侧是值values
print('\n',data.index)  # 使用.index属性查看索引
print('\n',data.values)  # 使用.values属性或者to_numpy()方法查看值（返回数组）

Sub-Category
Accessories    41936.6357
Appliances     18138.0054
Art             6527.7870
Binders        30221.7633
Bookcases      -3472.5560
Chairs         26590.1663
Copiers        55617.8249
Envelopes       6964.1767
Fasteners        949.5182
Furnishings    13059.1436
Labels          5546.2540
Machines        3384.7569
Paper          34053.5693
Phones         44515.7306
Storage        21278.8264
Supplies       -1189.0995
Tables        -17725.4811
Name: Profit, dtype: float64

 Index(['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs',
       'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels',
       'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables'],
      dtype='object', name='Sub-Category')

 [ 41936.6357  18138.0054   6527.787   30221.7633  -3472.556   26590.1663
  55617.8249   6964.1767    949.5182  13059.1436   5546.254    3384.7569
  34053.5693  44515.7306  21278.8264  -1189.0995 -17725.4811]


# Step3. 绘制水平柱状图
import plotly.graph_objects as go

fig = go.Figure(data=go.Bar(
    y=data.index,
    x=data.values,
    marker=dict(
        color=data.values,
        colorscale=[[0,'#fb8c00'],[0.25,'white'],[1,'#1565c0']],
        showscale=True),
    orientation='h'
))

fig.update_layout(
    title='Profit by Sub-Category',
    plot_bgcolor='white',  
    xaxis=dict(title='Profit',gridcolor='rgba(0,0,0,0.05)'),
    yaxis=dict(title='Sub-Category')
)

fig.show()


# 思考：如何设置一个利润阈值？柱形的颜色由是否超过阈值来决定

thred = 25000
colors=['#BAB0AC']*data.shape[0]
for i in range(data.shape[0]):
    if data[i]>thred:
        colors[i]='#E15759'

fig = go.Figure(data=go.Bar(
    y=data.index,
    x=data.values,
    marker=dict(color=colors),
    orientation='h'
))

fig.update_layout(
    title='Profit by Sub-Category',
    plot_bgcolor='white',  
    xaxis=dict(title='Profit',gridcolor='rgba(0,0,0,0.05)'),
    yaxis=dict(title='Sub-Category'),
)

fig.show()


# Step1. 导入数据文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('./Sample - Superstore.xls',sheet_name='Orders')
df.head()


# Step2. 数据分析：从绘图角度来思考，一个图形fig中有三个trace（Segment），每个trace对应多个柱形（Sub-Category）
# 对Segment和Sub-Category两个指标进行分组，再对每组的Profit求和（数据聚合与分组操作——GroupBy机制）
data = df.groupby(['Segment','Sub-Category'])['Profit'].sum()
# df['Profit'].groupby([df['Segment'],df['Sub-Category']]).sum()  # 另一种写法
data

Segment      Sub-Category
Consumer     Accessories     20735.9225
             Appliances       6981.9282
             Art              3454.3011
             Binders         17995.5972
             Bookcases       -4435.6382
             Chairs          13235.3319
             Copiers         24083.7106
             Envelopes        3264.4126
             Fasteners         576.8008
             Furnishings      7919.4227
             Labels           3075.9884
             Machines         2141.0618
             Paper           15534.6436
             Phones          23837.1147
             Storage          7104.2004
             Supplies        -1657.5513
             Tables          -9728.0378
Corporate    Accessories     12707.4805
             Appliances       7429.8952
             Art              2004.6477
             Binders          6377.3201
             Bookcases         638.4502
             Chairs           8344.6565
             Copiers         18990.2789
             Envelopes        2571.2290
             Fasteners         251.9030
             Furnishings      3508.2077
             Labels           1760.8273
             Machines          703.0190
             Paper           10361.5468
             Phones          11766.2196
             Storage          9131.0247
             Supplies          338.9264
             Tables          -4906.4986
Home Office  Accessories      8493.2327
             Appliances       3726.1820
             Art              1068.8382
             Binders          5848.8460
             Bookcases         324.6320
             Chairs           5010.1779
             Copiers         12543.8354
             Envelopes        1128.5351
             Fasteners         120.8144
             Furnishings      1631.5132
             Labels            709.4383
             Machines          540.6761
             Paper            8157.3789
             Phones           8912.3963
             Storage          5043.6013
             Supplies          129.5254
             Tables          -3090.9447
Name: Profit, dtype: float64


# 得到的结果是一个MultiIndex（多重索引）的Series：左侧是两层索引index，右侧是值values
# data.index  # 使用index属性查看多重索引
print(data.index.levels[0])  # 查看外层索引
print(data.index.levels[1])  # 查看内层索引
print(data['Consumer'])  # 指定任一外层索引，查看内层Series

Index(['Consumer', 'Corporate', 'Home Office'], dtype='object', name='Segment')
Index(['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs',
       'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels',
       'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables'],
      dtype='object', name='Sub-Category')
Sub-Category
Accessories    20735.9225
Appliances      6981.9282
Art             3454.3011
Binders        17995.5972
Bookcases      -4435.6382
Chairs         13235.3319
Copiers        24083.7106
Envelopes       3264.4126
Fasteners        576.8008
Furnishings     7919.4227
Labels          3075.9884
Machines        2141.0618
Paper          15534.6436
Phones         23837.1147
Storage         7104.2004
Supplies       -1657.5513
Tables         -9728.0378
Name: Profit, dtype: float64


# Step3. 绘制堆积柱状图
import plotly.graph_objects as go

fig=go.Figure()

# 使用for循环来遍历segment，绘制多个trace
for i in data.index.levels[0]: 
    fig.add_trace(
        go.Bar(
            x=data[i].index,  # 当前segment对应Series的索引
            y=data[i].values,  # 当前segment对应Series的值
            name=i  # 当前segment名称
        ))
    
fig.update(layout=dict(
    title='Profit by Sub-Category & Segment',
    yaxis_title='Profit',
    barmode='stack'
))

fig.show()


# 思考：如何自定义颜色？

fig=go.Figure()

colors=['#76B7B2','#BAB0AC','#FF9DA7']  # 自定义颜色列表（离散值）

# 使用for循环来遍历segment，绘制多个trace
for i,c in zip(data.index.levels[0],colors):   # 使用zip()函数同时遍历两个序列
    fig.add_trace(
        go.Bar(
            x=data[i].index,  # 当前segment对应Series的索引
            y=data[i].values,  # 当前segment对应Series的值
            name=i,  # 当前segment名称
            marker=dict(color=c)  # 将离散颜色值传递给color参数
        ))

fig.update(layout=dict(
    title='Profit by Sub-Category & Segment',
    barmode='stack',
))

fig.show()


# Step1. 导入数据：导入文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
df.shape

(9994, 21)


# Step2. 数据筛选：对'Sub-Category'中'Paper'产品进行筛选
data = df[df['Sub-Category']=='Paper']  # 布尔值索引
data.shape

(1370, 21)


data.head()  # 查看筛选的结果


# Step3. 绘制散点图
import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
    x=data['Sales'],
    y=data['Profit'],
    mode='markers',
    marker=dict(
        color=data['Discount'],  # 用颜色表示 Discount
        size=12 ),  
    text=data['Discount'],
    opacity=0.7
))

fig.update_layout(
    title='Sales and Profit Distrubution of Paper',
    xaxis=dict(title='Sales'),
    yaxis=dict(title='Profit')
)

fig.show()


# 延伸；用气泡的颜色来展示不同地区（Region）的数据点
fig = go.Figure()

for i in data['Region'].unique():
    fig.add_trace(go.Scatter(
        x = data.loc[data['Region']==i,'Sales'],
        y = data.loc[data['Region']==i,'Profit'],
        mode='markers',
        name=i,
        marker_size=12,
        opacity=0.7
    ))

fig.update_layout(
    title='Sales and Profit Distrubution of Paper by Region',
    xaxis=dict(title='Sales'),
    yaxis=dict(title='Profit')
)

fig.show()


# Step1. 导入数据：导入文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
df.shape

(9994, 21)


# Step2. 数据分析得到销售额最高的前50名客户的销售额（Sales）、利润（Profit）和折扣（Discount）
data = df.groupby('Customer Name').agg({'Sales':'sum','Discount':'mean','Profit':'sum'})
data = data.sort_values(by='Sales',ascending=False)
data = data[:50]
data


# Step3. 绘制气泡图
import plotly.graph_objects as go

text=[]
for i in range(50):
    text.append(('Customer Name: {0}<br>Discount: {1:.2%}').format(data.index[i],data.iloc[i,1]))

fig = go.Figure(go.Scatter(
    x=data['Sales'],
    y=data['Profit'],
    mode='markers',
    marker=dict(
        color=data['Discount'],
        colorscale='viridis',
        showscale=True,
        size=data['Discount'],
        sizeref=2*max(data['Discount'])/(10**2)),
    text=text,
))

fig.update_layout(
    title='Top-50 Customers\' Sales and Profit',
    xaxis_title='Sales',
    yaxis_title='Profit'
)

fig.show()


# 导入数据并进行数据分析
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')

# 每天销售额和利润
data = df.groupby('Order Date')[['Sales','Profit']].sum()
data


# 索引和切片
print(data.loc['2018'],'\n')  # 2018年的记录
print(data.loc['2018-02'],'\n')  # 2018年2月的记录
print(data['2018-12-01':'2018-12-15'])  # 切片

                Sales    Profit
Order Date                     
2018-01-01  1481.8280 -181.4109
2018-01-02  2079.5540 -207.0473
2018-01-03  2070.2720  704.2800
2018-01-06    33.7400   15.5204
2018-01-07  3395.5900  758.7192
...               ...       ...
2018-12-26   814.5940   61.1202
2018-12-27   177.6360  -31.9742
2018-12-28  1657.3508  253.1188
2018-12-29  2915.5340  644.4338
2018-12-30   713.7900  101.5365

[322 rows x 2 columns] 

                Sales    Profit
Order Date                     
2018-02-02   913.3540  170.6770
2018-02-03   922.3270  215.5700
2018-02-04    32.6700    8.4942
2018-02-05  2263.0120   74.8820
2018-02-06   904.3540  204.3158
2018-02-09   773.7640 -411.9726
2018-02-10   227.1030   28.1274
2018-02-11  1241.5160  130.1018
2018-02-13  1058.4300  424.3345
2018-02-16  1337.4420   95.9756
2018-02-17  2964.8174 -383.5478
2018-02-18   287.3260   62.4082
2018-02-19  1314.5900  377.0515
2018-02-20  1150.2900 -107.5121
2018-02-21    47.9040   -2.9940
2018-02-23   117.8000   42.3700
2018-02-24  1448.6760  249.3929
2018-02-25   430.4920  -19.3798
2018-02-26  2847.6460  447.3532
2018-02-28    17.6200    8.2242 

               Sales     Profit
Order Date                     
2018-12-01  5331.178   718.8920
2018-12-02  9951.182    -7.3410
2018-12-03  1403.842   280.7407
2018-12-04  2639.638   -21.9881
2018-12-05  1453.136   447.6235
2018-12-06    10.680     2.8836
2018-12-07  2916.514 -2686.6673
2018-12-08  7643.041  1154.6045
2018-12-09  5470.390  1487.1418
2018-12-10  3873.559   715.5696
2018-12-11  2823.965   -82.4089
2018-12-13   580.936    99.2154
2018-12-14  3897.714   215.2500
2018-12-15   306.888    52.5946


# 绘制时间序列图：2018年每天的销售额（Sales）和利润（Profit）
import plotly.graph_objects as go
import pandas as pd

data = df.groupby('Order Date')[['Sales','Profit']].sum()
data = data.loc['2018']

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=data.index,
    y=data['Sales'],
    name='Sales'
))
fig.add_trace(go.Scatter(
    x=data.index,
    y=data['Profit'],
    name='Profit'
))

fig.update_traces(opacity=0.8)

fig.update_layout(
    title='Sales and Profit in 2018'
)

fig.show()


# 重新采样
data = df.groupby('Order Date')[['Sales','Profit']].sum()
data = data.loc['2018'].resample('M').sum()
data


data.index.strftime('%Y-%m')  # 转换时间格式

Index(['2018-01', '2018-02', '2018-03', '2018-04', '2018-05', '2018-06',
       '2018-07', '2018-08', '2018-09', '2018-10', '2018-11', '2018-12'],
      dtype='object', name='Order Date')


# 绘制时间序列图：2018年每个月的销售额（Sales）和利润（Profit）

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=data.index.strftime('%Y-%m'),
    y=data['Sales'],
    name='Sales',
    marker_color='#ff7043'
))
fig.add_trace(go.Scatter(
    x=data.index.strftime('%Y-%m'),
    y=data['Profit'],
    name='Profit',
    marker_color='#29b6f6'
))
        
fig.update_layout(
    title='Sales and Profit in 2018',
    xaxis=dict(dtick='M1')  # X轴刻度显示为每一个月
)

fig.show()


# 数据分析：得到每个地区的销售额总和
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
data = df.groupby('Region')['Sales'].sum()
data

Region
Central    501239.8908
East       678781.2400
South      391721.9050
West       725457.8245
Name: Sales, dtype: float64


# 绘制饼图，并在饼图上显示地区和占比
import plotly.graph_objects as go

fig = go.Figure(data = go.Pie(
    labels = data.index, 
    values = data.values,
    textinfo = 'label+percent',
    opacity = 0.9
))

fig.update_layout(
    title='Sales by Region',
)

fig.show()


# 数据分析：得到2015年和2018年每个地区的销售额总和
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
data1 = df[df['Order Date'].array.year==2015]
data1 = data1.groupby('Region')['Sales'].sum()

data2 = df[df['Order Date'].array.year==2018]
data2 = data2.groupby('Region')['Sales'].sum()

# 将两组数据进行合并，赋值给data变量
data = pd.merge(data1,data2,left_index = True,right_index = True, suffixes = ('_2015','_2018'))
data


# 绘制多个饼图 Pie Charts in subplots 
# 方法1. 使用domain参数设置每个Pie的区域
import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Pie(
    labels = data.index, 
    values = data['Sales_2015'], 
    name = 'Sales in 2015',
    domain = dict(x = [0,0.5])  # 设置domain参数
))

fig.add_trace(go.Pie(
    labels = data.index, 
    values = data['Sales_2018'],
    name = 'Sales in 2018',
    domain = dict(x = [0.5,1])  # 设置domain参数
))

fig.update_traces(
    opacity=0.9,
    hole=0.4,
    # scalegroup 如果多个trace属于同一个scalegroup，将会根据总数值来显示面积比例
    scalegroup='one'
)

fig.update_layout(title = 'Sales by Region')

fig.show()


# 方法2. 使用make_subplots方法创建多子图
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# 使用make_subplots方法创建多子图
fig = make_subplots(rows = 1, cols = 2,  # 设置行数和列数
                    specs = [[{'type':'domain'}, {'type':'domain'}]],  # 设置每个子图的类型，当绘制饼图时类型为domain
                    subplot_titles=['Sales in 2015','Sales in 2018'])  # 设置每个子图的标题

fig.add_trace(trace = go.Pie( labels = data.index, 
                              values = data['Sales_2015']),
              row = 1, col = 1 )  # 设置该子图的位置


fig.add_trace(trace = go.Pie( labels = data.index, 
                              values = data['Sales_2018']),
              row = 1, col = 2 )  # 设置该子图的位置

fig.update_traces(
    opacity=0.9,
    hole=0.4,
    # scalegroup 如果多个trace属于同一个scalegroup，将会根据总数值来显示面积比例
    scalegroup='two'
)

fig.update_layout(title = 'Sales by Region')

fig.show()


# 数据分析：得到每个类别-子类别的销售额总和
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
# 先对'Category'-'Sub-Category'进行分组，计算每组'Sales'总和
data = df.groupby(['Category', 'Sub-Category'])['Sales'].sum()
data  # 得到一个Multi-Index的Series

Category         Sub-Category
Furniture        Bookcases       114879.9963
                 Chairs          328449.1030
                 Furnishings      91705.1640
                 Tables          206965.5320
Office Supplies  Appliances      107532.1610
                 Art              27118.7920
                 Binders         203412.7330
                 Envelopes        16476.4020
                 Fasteners         3024.2800
                 Labels           12486.3120
                 Paper            78479.2060
                 Storage         223843.6080
                 Supplies         46673.5380
Technology       Accessories     167380.3180
                 Copiers         149528.0300
                 Machines        189238.6310
                 Phones          330007.0540
Name: Sales, dtype: float64


# 绘制的旭日图有两层，内层是Category（父节点3个），外层是Sub-Category（子节点）
labels = list(data.index.levels[0])  # labels列表包括所有节点标签，初值为3个父节点
parents = ['']*3  # parents列表初值为3个根节点，即空字符串

for (i1,i2) in data.index:  # 遍历data的多层索引
    labels.append(i2)  # 通过循环向labels列表添加子节点
    parents.append(i1)  # 通过循环向parents列表添加子节点的父节点，即多个重复的Category

print('labels:',labels)
print('\nparents:',parents)

labels: ['Furniture', 'Office Supplies', 'Technology', 'Bookcases', 'Chairs', 'Furnishings', 'Tables', 'Appliances', 'Art', 'Binders', 'Envelopes', 'Fasteners', 'Labels', 'Paper', 'Storage', 'Supplies', 'Accessories', 'Copiers', 'Machines', 'Phones']

parents: ['', '', '', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Technology', 'Technology', 'Technology', 'Technology']


# valuaes列表是每个labels对应的数值，因此是3个Category的Sales总和 + 每个Sub-Category的Sales总和
values = list(data.groupby(level='Category').sum())+list(data.values)
print('values:',values)

values: [741999.7953, 719047.032, 836154.033, 114879.9963, 328449.103, 91705.164, 206965.532, 107532.161, 27118.792, 203412.733, 16476.402000000002, 3024.28, 12486.312, 78479.206, 223843.608, 46673.538, 167380.318, 149528.03, 189238.631, 330007.054]


# 绘制旭日图
import plotly.graph_objects as go

fig = go.Figure(data = go.Sunburst(
    labels = labels,
    parents = parents,
    values = values,
    branchvalues='total',
    # 可选参数包括['label', 'text', 'value', 'current path', 'percent root', 'percent entry', 'percent parent'] 
    textinfo ='label + percent parent'   # 显示标签和所占父节点的百分比
))

fig.update_layout(
    title='Sales by Sub-Category',
    margin = dict(l=0, r=0, b=0)
)

fig.show()


# 数据分析：得到每个类别-子类别的销售额总和
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
# 先对'Category'-'Sub-Category'进行分组，计算每组'Sales'总和
data = df.groupby(['Category', 'Sub-Category'])['Sales'].sum()
data  # 得到一个Multi-Index的Series

Category         Sub-Category
Furniture        Bookcases       114879.9963
                 Chairs          328449.1030
                 Furnishings      91705.1640
                 Tables          206965.5320
Office Supplies  Appliances      107532.1610
                 Art              27118.7920
                 Binders         203412.7330
                 Envelopes        16476.4020
                 Fasteners         3024.2800
                 Labels           12486.3120
                 Paper            78479.2060
                 Storage         223843.6080
                 Supplies         46673.5380
Technology       Accessories     167380.3180
                 Copiers         149528.0300
                 Machines        189238.6310
                 Phones          330007.0540
Name: Sales, dtype: float64


# 该层级图有两层，外部矩形代表父类别（Category），内部矩形代表子类别（Sub-Category）
labels = list(data.index.levels[0])  # labels列表包括所有节点标签，初值为3个父节点
parents = ['']*3  # parents列表初值为3个根节点，即空字符串

for (i,v) in data.index:  # 遍历data的多层索引
    labels.append(v)  # 通过循环向labels列表添加子节点
    parents.append(i)  # 通过循环向parents列表添加子节点对应的父节点，即多个重复的Category

print('labels:',labels)
print('\nparents:',parents)

labels: ['Furniture', 'Office Supplies', 'Technology', 'Bookcases', 'Chairs', 'Furnishings', 'Tables', 'Appliances', 'Art', 'Binders', 'Envelopes', 'Fasteners', 'Labels', 'Paper', 'Storage', 'Supplies', 'Accessories', 'Copiers', 'Machines', 'Phones']

parents: ['', '', '', 'Furniture', 'Furniture', 'Furniture', 'Furniture', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Office Supplies', 'Technology', 'Technology', 'Technology', 'Technology']


# values列表是每个labels对应的数值，因此是3个Category的Sales总和 连接（+） 每个Sub-Category的Sales总和
values = list(data.groupby(level='Category').sum())+list(data.values)
print('values:',values)

values: [741999.7953, 719047.032, 836154.033, 114879.9963, 328449.103, 91705.164, 206965.532, 107532.161, 27118.792, 203412.733, 16476.402000000002, 3024.28, 12486.312, 78479.206, 223843.608, 46673.538, 167380.318, 149528.03, 189238.631, 330007.054]


# 绘制层级图：将三个不同的Category设置成三种离散的颜色
fig = go.Figure(go.Treemap(
    labels = labels,
    parents = parents,
    values = values,
    branchvalues='total',
    textinfo ='label + value + percent parent',   # 显示标签、值以及所占父节点的百分比
    root_color = 'white'
))

fig.update_layout(
    title='Sales by Sub-Category',
    treemapcolorway = ['lightblue','lightgrey','pink']
)

fig.show()


# 绘制层级图：将每个标签所在板块的颜色随着销量的值深浅变化
fig = go.Figure(go.Treemap(
    labels = labels,
    parents = parents,
    values = values,
    branchvalues='total',
    textinfo ='label + value + percent parent',  # 显示标签、值以及所占父节点的百分比
    marker = dict(colors = values, colorscale = 'Greens',showscale = True),
))

fig.update_layout(
    title='Sales by Sub-Category',
)

fig.show()


import numpy as np
import plotly.graph_objects as go

# 先对'Region'-'State'进行分组，计算每组'Sales'总和
data = df.groupby(['Region','State'])[['Sales','Profit']].sum() 

# 绘制的旭日图有两层，内层是Region（父节点4个），外层是State（子节点）
labels = list(data.index.levels[0])  # labels列表包括所有节点标签，初值为4个父节点
parents = ['']*4  # len(data.index.levels[0])  # parents列表初值为4个根节点，即空字符串
for (i1,i2) in data.index:  # 遍历data的多层索引
    labels.append(i2)   # 通过循环向labels列表添加子节点
    parents.append(i1)  # 通过循环向parents列表添加子节点的父节点，即多个重复的Region

# valuaes列表是每个labels对应的Sales总和，因此是4个Region的Sales+每个States的Sales
values = list(data['Sales'].groupby(level='Region').sum()) + list(data['Sales'].values)
# colors列表是每个labels对应的Profit总和，因此是4个Region的Profit+每个States的Profit
colors = list(data['Profit'].groupby(level='Region').sum()) + list(data['Profit'].values)

# text列表用于交互时显示的文本，同colors列表，也可使用for循环写成自定义样式
text = []
for i in range(len(labels)):
    text.append(('Profit:{0}').format(np.round(colors[i],2)))

fig = go.Figure(data = go.Treemap(
    labels = labels,
    parents = parents,
    values = values,
    branchvalues = 'total',
    marker = dict(colors = colors, 
                  colorscale = [[0,'#f57c00'],[0.2,'white'],[1,'#1565c0']],
                  showscale = True),
    # 可选参数包括['label', 'text', 'value', 'current path', 'percent root', 'percent entry', 'percent parent'] 
    text=text,
    hoverinfo ='value + percent parent + text',   # 交互时显示值和所占父节点的百分比
    textinfo ='label + percent root',  # 文本显示标签和所占根节点百分比
))

fig.update_layout(
    title = 'Sales and Profit by Region & States',
    margin = dict(l=0, r=0, b=0)
)

fig.show()


# 数据分析：得到每笔订单的利润之和
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
data = df.groupby('Order ID')['Profit'].sum()
data  # 共有5009个订单，远小于记录数

Order ID
CA-2015-100006    109.6113
CA-2015-100090    -19.0890
CA-2015-100293     31.8696
CA-2015-100328      1.3257
CA-2015-100363      7.7192
                    ...   
US-2018-168802      5.9696
US-2018-169320     16.6721
US-2018-169488     26.5552
US-2018-169502     32.4527
US-2018-169551    -62.2895
Name: Profit, Length: 5009, dtype: float64


# 绘制直方图
import plotly.graph_objects as go

fig = go.Figure(go.Histogram(
    x = data.values,
    xbins = dict(
        start=-500,
        end=500,
        size=50
    ),
    marker_color='#EB89B5',
    opacity=0.8
))

fig.update_layout(
    title='Distribution of Orders\' Profit',
    xaxis=dict(title='Profit'),
    yaxis=dict(title='Count')
)

fig.show()


# 数据分析：分别得到产品子类别（Sub-Category）为'Chairs'和'Tables'的每一条记录的利润（Profit），不做聚合
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
data1 = df.loc[df['Sub-Category']=='Chairs','Profit']
data2 = df.loc[df['Sub-Category']=='Tables','Profit']
print('Number of \'Chairs\' records: ', data1.shape)
print('Number of \'Tables\' records: ', data2.shape)

Number of 'Chairs' records:  (617,)
Number of 'Tables' records:  (319,)


# 绘制重叠直方图
import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Histogram(
    x = data1,
    name = 'Chairs'
))
fig.add_trace(go.Histogram(
    x = data2,
    name = 'Tables'
))

fig.update_layout(
    barmode='overlay',
    title='Profit Distribution of Chairs and Tables',
    xaxis=dict(title='Profit'),
    yaxis=dict(title='Percent')
)

fig.update_traces(
    histnorm='probability',
    opacity=0.7,
    xbins=dict(
        start=-800,
        end=800,
        size=50
    ),
)

fig.show()


# 数据分析：得到不同用户细分（Segment）中，每笔订单的利润（Profit）之和
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
data = df[df['Order Date'].array.year == 2018]
data = data.groupby(['Segment','Order ID'])['Profit'].sum()
data

Segment      Order ID      
Consumer     CA-2018-100013      12.2162
             CA-2018-100097     302.4705
             CA-2018-100111    1571.7964
             CA-2018-100160      16.9641
             CA-2018-100230      42.3643
                                 ...    
Home Office  US-2018-162558   -1034.8361
             US-2018-163790     282.4405
             US-2018-166037     157.4685
             US-2018-166324     -52.0700
             US-2018-169502      32.4527
Name: Profit, Length: 1687, dtype: float64


# 绘制分布图
import plotly.figure_factory as ff

hist_data = []
group_labels = []
colors = ['#393E46', '#2BCDC1', '#F66095']  # 自定义颜色列表

for i in data.index.levels[0]:
    hist_data.append(data[i])
    group_labels.append(i)

fig = ff.create_distplot(
    hist_data,
    group_labels,
    bin_size = 10,
    colors = colors,
    show_curve=False,
)

fig.update_layout(
    title = 'Profit Distribution by Segment in 2018',
    xaxis = dict(range=[-200,200],title='Profit'),
    yaxis = dict(title='Probability Density')
)

fig.show()

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
0	1	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600	2	0.00	41.9136
1	2	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400	3	0.00	219.5820
2	3	CA-2017-138688	2017-06-12	2017-06-16	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	...	90036.0	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200	2	0.00	6.8714
3	4	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775	5	0.45	-383.0310
4	5	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680	2	0.20	2.5164

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
0	1	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600	2	0.00	41.9136
1	2	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	42420.0	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400	3	0.00	219.5820
2	3	CA-2017-138688	2017-06-12	2017-06-16	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	...	90036.0	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200	2	0.00	6.8714
3	4	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775	5	0.45	-383.0310
4	5	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	33311.0	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680	2	0.20	2.5164

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
12	13	CA-2018-114412	2018-04-15	2018-04-20	Standard Class	AA-10480	Andrew Allen	Consumer	United States	Concord	...	28027.0	South	OFF-PA-10002365	Office Supplies	Paper	Xerox 1967	15.552	3	0.2	5.4432
34	35	CA-2018-107727	2018-10-19	2018-10-23	Second Class	MA-17560	Matt Abelman	Home Office	United States	Houston	...	77095.0	Central	OFF-PA-10000249	Office Supplies	Paper	Easy-staple paper	29.472	3	0.2	9.9468
56	57	CA-2017-111682	2017-06-17	2017-06-18	First Class	TB-21055	Ted Butterfield	Consumer	United States	Troy	...	12180.0	East	OFF-PA-10001569	Office Supplies	Paper	Xerox 232	32.400	5	0.0	15.5520
58	59	CA-2017-111682	2017-06-17	2017-06-18	First Class	TB-21055	Ted Butterfield	Consumer	United States	Troy	...	12180.0	East	OFF-PA-10000587	Office Supplies	Paper	Array Parchment Paper, Assorted Colors	14.560	2	0.0	6.9888
64	65	CA-2016-135545	2016-11-24	2016-11-30	Standard Class	KM-16720	Kunst Miller	Consumer	United States	Los Angeles	...	90004.0	West	OFF-PA-10003892	Office Supplies	Paper	Xerox 1943	146.730	3	0.0	68.9631

	Sales	Discount	Profit
Customer Name
Sean Miller	25043.0500	0.246667	-1980.7393
Tamara Chand	19052.2180	0.116667	8981.3239
Raymond Buch	15117.3390	0.094444	6976.0959
Tom Ashbrook	14595.6200	0.080000	4703.7883
Adrian Barton	14473.5710	0.240000	5444.8055
Ken Lonsdale	14175.2290	0.200000	806.8550
Sanjit Chand	14142.3340	0.063636	5757.4119
Hunter Lopez	12873.2980	0.018182	5622.4292
Sanjit Engle	12209.4380	0.110526	2650.6769
Christopher Conant	12129.0720	0.281818	2177.0493
Todd Sumrall	11891.7510	0.116667	2371.7144
Greg Tran	11820.1200	0.100000	2163.4269
Becky Martin	11789.6300	0.168750	-1659.9581
Seth Vernon	11470.9500	0.156250	1199.4242
Caroline Jumper	11164.9740	0.188500	858.7414
Clay Ludtke	10880.5460	0.114286	1933.7831
Maria Etezadi	10663.7280	0.131818	1859.4695
Karen Ferguson	10604.2660	0.033333	1660.1386
Bill Shonely	10501.6530	0.011111	2616.0644
Edward Hooks	10310.8800	0.071875	1393.5154
John Lee	9799.9230	0.088235	228.9070
Grant Thornton	9351.2120	0.250000	-4108.6589
Helen Wasserman	9300.2540	0.045000	2164.1611
Tom Boeckenhauer	9133.9900	0.070588	2798.3689
Peter Fuller	9062.8640	0.121053	-614.2943
Christopher Martinez	8954.0200	0.120000	3899.8904
Justin Deggeller	8828.0305	0.055882	1619.5199
Joe Elijah	8697.8430	0.322727	1262.2926
Laura Armstrong	8673.2220	0.115385	2059.1199
Pete Kriz	8646.9340	0.076000	2038.2676
Daniel Raglin	8350.8680	0.153846	2869.0760
Natalie Fritzler	8322.8260	0.250000	-1695.9714
Karen Daniels	8282.3580	0.187500	1107.6952
Nick Crebassa	8241.7390	0.136667	1314.7580
Harry Marie	8236.7648	0.231000	2437.9836
Keith Dawkins	8181.2560	0.087500	3038.6254
Sean Braxton	8057.8910	0.241176	-2082.7451
Zuschuss Carroll	8025.7070	0.254839	-1032.1490
Joseph Holt	7954.9980	0.085714	-644.6982
Nora Preis	7903.1825	0.196154	631.2282
Anna Häberlin	7888.2940	0.217391	1298.0166
Adam Bellavance	7755.6200	0.044444	2054.5885
Jim Epp	7754.9760	0.160000	1623.4019
Jane Waco	7721.7140	0.071429	2173.7094
Lena Creighton	7663.1260	0.156522	1288.3469
John Murray	7625.0760	0.184615	1574.6164
Jonathan Doherty	7610.8640	0.075000	1050.2668
Patrick O'Brill	7473.8282	0.210000	38.4757
Maribeth Schnelling	7443.6900	0.160417	844.9355
Rick Wilson	7397.4010	0.134783	1586.6273

	Sales	Profit
Order Date
2018-01-31	43971.3740	7140.4391
2018-02-28	20301.1334	1613.8720
2018-03-31	58872.3528	14751.8915
2018-04-30	36521.5361	933.2900
2018-05-31	44261.1102	6342.5828
2018-06-30	52981.7257	8223.3357
2018-07-31	45264.4160	6952.6212
2018-08-31	63120.8880	9040.9557
2018-09-30	87866.6520	10991.5556
2018-10-31	77776.9232	9275.2755
2018-11-30	118447.8250	9690.1037
2018-12-31	83829.3188	8483.3468

目录

练习1. 绘制水平柱状图 Profit by Sub-Category¶

练习2. 绘制堆积柱状图 Profit by Sub-Category & Segment¶

练习3. 绘制散点图 Sales and Profit Distrubution¶

练习4. 绘制气泡图 Sales and Profit Distrubution¶

练习5. 绘制时间序列图 Sales and Profit by day¶

练习6. 绘制时间序列图 Sales and Profit by month¶

练习7. 绘制饼图 Sales by Region¶

练习8. 绘制多个饼图 Sales by Region¶

练习9. 绘制旭日图 Sales by Sub-Category¶

练习10. 绘制层级图 Sales by Sub-Category¶

练习11. 绘制层级图 Sales and Profit by Region & States¶

练习12. 绘制直方图 Distribution of Orders' Profit¶

练习13. 绘制重叠直方图 Profit Distribution of Chairs and Tables¶

练习14. 绘制分布图 Profit Distribution by Segment¶

	Sales	Profit
Order Date
2015-01-03	16.4480	5.5512
2015-01-04	288.0600	-65.9901
2015-01-05	19.5360	4.8840
2015-01-06	4407.1000	1358.0524
2015-01-07	87.1580	-71.9621
...	...	...
2018-12-26	814.5940	61.1202
2018-12-27	177.6360	-31.9742
2018-12-28	1657.3508	253.1188
2018-12-29	2915.5340	644.4338
2018-12-30	713.7900	101.5365

	Sales_2015	Sales_2018
Region
Central	103838.1646	147098.1282
East	128680.4570	213082.9040
South	103845.8435	122905.8575
West	147883.0330	250128.3655